openni

I am trying to receive a depth image from an Orbbec Astra Pro camera connected to a Windows 10 machine. I have therfor installed opencv-python 4.0.0.21 and primesense 2.2.0.30.post5 which seems to be latest stable python packages available. This is the code snippet I am experimenting with: import numpy as np import cv2 from primesense import openni2 from primesense import _openni2 as c_api openni2.initialize("./OpenNI-Windows-x64-2.3/Redist") if openni2.is_initialized(): print('openni2 ready') else: print('openni2 not ready') dev = openni2.Device.open_any() depth_stream = dev.create_depth

I was looking over the documentation trying to find anything that will allow me the Kinect/device? I'm trying to get accelerometer data, but not sure how. So far there were two things I've spotted in the guide and docs: XnModuleDeviceInterface/xn::ModuleDevice and XnModuleLockAwareInterface/xn::ModuleLockAwareInterface . I'm wondering if I can use the ModuleDevice Get/Set methods to talk to the device and ask for accelerometer data. If so, how can I get started? Also, I was thinking, if it would be possible to 'lock' openni functionality temporarily while I try to get accelerometer data via

We captured a 3d Image using Kinect with OpenNI Library and got the rgb and depth images in the form of OpenCV Mat using this code. main() { OpenNI::initialize(); puts( "Kinect initialization..." ); Device device; if ( device.open( openni::ANY_DEVICE ) != 0 ) { puts( "Kinect not found !" ); return -1; } puts( "Kinect opened" ); VideoStream depth, color; color.create( device, SENSOR_COLOR ); color.start(); puts( "Camera ok" ); depth.create( device, SENSOR_DEPTH ); depth.start(); puts( "Depth sensor ok" ); VideoMode paramvideo; paramvideo.setResolution( 640, 480 ); paramvideo.setFps( 30 );

Let me explain my need before I explain the problem. I am looking forward for a hand controlled application. Navigation using palm and clicks using grab/fist. Currently, I am working with Openni, which sounds promising and has few examples which turned out to be useful in my case, as it had inbuild hand tracker in samples. which serves my purpose for time being. What I want to ask is, 1) what would be the best approach to have a fist/grab detector ? I trained and used Adaboost fist classifiers on extracted RGB data, which was pretty good, but, it has too many false detections to move forward.